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Steady-State Motion Visual Evoked Potential (SSMVEP) Enhancement Method Based on Time-Frequency Image Fusion
Joint Authors
Xu, Guanghua
Yan, Wenqiang
Chen, Longting
Zheng, Xiaowei
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-21
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
The steady-state motion visual evoked potential (SSMVEP) collected from the scalp suffers from strong noise and is contaminated by artifacts such as the electrooculogram (EOG) and the electromyogram (EMG).
Spatial filtering methods can fuse the information of different brain regions, which is beneficial for the enhancement of the active components of the SSMVEP.
Traditional spatial filtering methods fuse electroencephalogram (EEG) in the time domain.
Based on the idea of image fusion, this study proposed an SSMVEP enhancement method based on time-frequency (T-F) image fusion.
The purpose is to fuse SSMVEP in the T-F domain and improve the enhancement effect of the traditional spatial filtering method on SSMVEP active components.
Firstly, two electrode signals were transformed from the time domain to the T-F domain via short-time Fourier transform (STFT).
The transformed T-F signals can be regarded as T-F images.
Then, two T-F images were decomposed via two-dimensional multiscale wavelet decomposition, and both the high-frequency coefficients and low-frequency coefficients of the wavelet were fused by specific fusion rules.
The two images were fused into one image via two-dimensional wavelet reconstruction.
The fused image was subjected to mean filtering, and finally, the fused time-domain signal was obtained by inverse STFT (ISTFT).
The experimental results show that the proposed method has better enhancement effect on SSMVEP active components than the traditional spatial filtering methods.
This study indicates that it is feasible to fuse SSMVEP in the T-F domain, which provides a new idea for SSMVEP analysis.
American Psychological Association (APA)
Yan, Wenqiang& Xu, Guanghua& Chen, Longting& Zheng, Xiaowei. 2019. Steady-State Motion Visual Evoked Potential (SSMVEP) Enhancement Method Based on Time-Frequency Image Fusion. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1129693
Modern Language Association (MLA)
Yan, Wenqiang…[et al.]. Steady-State Motion Visual Evoked Potential (SSMVEP) Enhancement Method Based on Time-Frequency Image Fusion. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1129693
American Medical Association (AMA)
Yan, Wenqiang& Xu, Guanghua& Chen, Longting& Zheng, Xiaowei. Steady-State Motion Visual Evoked Potential (SSMVEP) Enhancement Method Based on Time-Frequency Image Fusion. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1129693
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129693